粒子群算法在极化SAR目标对比增强中的应用

摘要

According to the calculation complex problem of SAR optimization of polarimetric contract enhancement, the particle swarm optimization algorithm was proposed and was verified effectively through SAR data experiments in this paper. In this method, the polarization contrast enhancement problem was abstract into the optimal problem, taking the statistical area average covariance matrix as the input of the particle swarm optimization algorithm, calculated the fitness of each particle, and updated the individual optimum of each particle swarm and the global optimum through iterative, and get the optimal contrast. All of the experiments demonstrate that the contrast enhancement algorithm that based on the particle swarm optimization algorithm can achieve better contrast, and achieve comparatively simple.%针对SAR极化对比增强中存在计算复杂的问题,将粒子群算法应用到极化SAR目标增强当中,并通过数据实验,验证了其优越性.该方法将对比增强问题抽象成最优化问题,将统计得到的区域平均协方差矩阵作为粒子群优化算法的输入,计算每个粒子的适应度值,更新粒子群的个体最优和全局最优值,通过迭代得到最优对比度.实验结果表明,与传统的最优极化对比增强算法相比,基于粒子群优化算法的极化对比增强方法能达到更好的对比度,而且实现较为简单.